Enterprise AI Adoption = classic Change Management!

Andy Wood
August 2025
Renegade Agency

Many Enterprise AI transformation programs will fail because they ignore half the problem, the people. Specifically, the need to drive understanding, buy-in and real adoption.

In many ways the technology is the easy bit. As a Strategy Partner working on AI enablement, every organization I talk to is excited about the tech. Where businesses struggle to move forward is when they focus only on the technology, ignoring the people, workflows, and decision-making needed to make it work in the real world.

This is about good old fashioned Change Management. It’s about humans, and humans are hard, expensive, emotional and often behave in ways we don’t expect.

In this article, I’ll unpack the five most common mistakes organizations are making with AI adoption, and the alternative approaches that can deliver better results, including the agile, people-first framework we use in our Enterprise AI Proof-of-Value programs.

Common mistakes

Technology is seductive. It’s easy to believe we can use it to eliminate human idiosyncrasies.

But technology isn’t about fixing people. In reality it’s the human half of the human-technology dynamic that makes the whole system really work. And yet, in the rush to adopt AI, many businesses are focusing only on the technology, neglecting the human side. These AI programs will fail not because the tech isn’t good enough, but because the people side isn’t addressed.

Here are some of the common mistakes

1. Working in silos

Different stakeholders are chasing different things. IT wants security and integration. Operations wants zero disruption. Business owners want ROI. Everyone chasing different goals, with “forced consensus” that rarely equals real buy-in. In few cases are teams coming together to collectively define what “good” looks like.

2. Designing workflows from an ivory tower

AI strategies drawn up by people removed from the day-to-day realities. The marketing, editorial, and compliance teams who live in the workflows aren’t asked what’s actually broken. Businesses are building solutions for theoretical problems, while real bottlenecks remain invisible.

3. Sandbox pilots that avoid real-world mess

Testing in perfect, isolated environments misses the friction that only appears in live workflows. It’s easy to imagine that sandbox pilots are an easy way to avoid breaking things. But you only prove you can make AI work in a vacuum, independent of real life workflows.

4. Weak business cases

Headline-grabbing pilot metrics are a great way to motivate people and generate excitement. But they’re meaningless if you can’t also answer the bigger questions: How do we scale? What’s the cost? What resources are needed?

5. Tech FOMO

Analysis paralysis is a common problem, with teams losing months comparing tech tools. Then something new appears and the cycle begins all over again. Todays rate of change means you can never finish this task.

A better way?

The companies that are seeing the biggest success are the those that focus on more than just the technology. They’re redesigning how workflows operate with humans and AI working together, and aligning pilots with how the organization actually works and makes decisions in real life.

Bring humans into the design early:

If the people who will use the AI daily aren’t involved from day one, adoption will tank. Real buy-in comes from collaboration, not consensus imposed from above.

Test in real-world conditions:

Messy reality is where the real learning happens. Start small, and run pilots inside live workflows so you capture the real integration issues.

Take a holistic view of roles and data:

Workflow owners often don’t understand the data; data teams often don’t understand the workflows. Success comes from stitching these perspectives together.

Focus on the business problem, not the tool:

Ignore the shiny tech at first. Zero in on the business need, then find the tool that fits, not the other way around.

Start small to win big:

Lower-risk pilots let you iterate quickly, build a real business case, and create momentum for change.

Old Skool Transformation

I remember the early days of “digital transformation”. In 1997 I was working at Ford Motor Company inside their newly formed internet & New Media team.

This was a Value Creation Team, focused on developing pilots that would use the internet to re-engineer core engagements with customers. We had a remit to work across business silos, stitching together end-to-end workflows for things like direct vehicle sale and vehicle finance. Our work was grounded in real world business models, and day- two-day operational realities. We worked directly with dealerships, local marketing teams and customers, across all the “big 5 European markets”, bringing together cutting edge technology with deep ‘on the ground’ business experience.

But this approach was not common. And a lot of big consultancy projects crashed back then because such little thought was given to the humans, and to the real world workflows in the system. All process, powerpoint, and no people.

Those that succeeded, like Ford, did so by working across silos, piloting in real environments, and aligning the redesign of processes with operational realities.

Today the stakes are even higher

AI adoption today is no different. The major difference though, is that todays change is faster and touches more of the business all at once. The stakes are higher, the speed is greater, and the “trough of disillusionment” is deeper if you get it wrong.

Get started the right way

If you want to see early results and long-term adoption, your pilots need to:

Involve cross-functional teams from the start
Be run in real workflows, not just sandboxes
Produce evidence that can survive a business case review
Show both quick wins to drive confidence and the implications for scaling

It’s exactly why we built our Enterprise AI Agile Proof-of-Value approach. Pilot projects chunked into fast, agile 6 week sprints, designed to build momentum, trust, and measurable impact without overwhelming your teams.

Final thought.

AI is here to stay. The technology is ready, the question is, are your people?

Transition the discussion from the technology towards change and the real world implications of the technology.

Manage the change well and you’ll move faster, stay ahead, and avoid the classic traps that are stalling so many.

Continue the Conversation

Where do you see successes and failures in your AI Pilots? Are you running into similar challenges?

Andy Wood, Strategy Partner

20+ years bringing together business strategy, market insight, and technology to deliver business transformation, customer experience innovation and operational efficiency.

I can help you:

• Deliver next-generation digital experiences for customers

• Align enterprise technologies with operational & strategic goals

• Understand AI across business operations & customer experience